文献类型：期刊文献

英文题名：Infrastructure-Free Hierarchical Mobile Robot Global Localization in Repetitive Environments

作者：Wu, Zhenyu[1];Yue, Yufeng[2];Wen, Mingxing[1];Zhang, Jun[1];Yi, Jianjun[3];Wang, Danwei[1]

机构：[1]Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore;[2]Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2021

卷号：70

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20213010683670);WOS:【SCI-EXPANDED(收录号:WOS:000698642600026)】；

基金：This work was supported in part by the National Research Foundation, Singapore, under its Medium Sized Centre for Advanced Robotics Technology Innovation (CARTIN), and in part by Shanghai Science and Technology Action Plan under Grant 18DZ1204000 and Grant 18510745500. The Associate Editor coordinating the review process was Dr. Peter Xiaoping Liu.

语种：英文

外文关键词：Global localization; infrastructure-free; initialization; magnetic field (MF) measurement; repetitive features

摘要：Repetitive or ambiguous environment, where structures are highly similar and distinct geometric features are not sufficient, is one of the critical and challenging scenarios for mobile robots to perform global localization or simultaneous localization and mapping (SLAM) tasks. The robots are easy to get lost or mismatched to wrong places in such environments. The existing solutions either rely heavily on pre-installed infrastructures which are inflexible and expensive or rely on single sensorbased global localization methods whose initialization module is not capable enough to provide distinctive information. Thus, this article proposes a hierarchical probabilistic framework that addresses the problem of infrastructure-free mobile robot global localization in GPS-challenged repetitive environments by leveraging both the measured magnetic field (MF) and Light Detection and Ranging (LiDAR) information. The proposed hierarchical system mainly consists of: 1) coarse localization: MF-based localization and 2) fine localization: LiDAR-based localization (LBL). The ambient MF functions as a substitute for the GPS signal, which is considered not available or severely challenged in indoor/semi-indoor local environments. Based on the pre-built MF database, multiple coarse candidate robot poses can first be determined by the proposed initial pose estimation algorithm, which incorporates multivariate Gaussian observation model and random sample consensus (RANSAC) algorithm. Then, using the obtained multiple candidate poses as initialization, the robot can be localized more accurately by LiDAR-based fine localization. Extensive real-world experiments demonstrate that the proposed system achieves over 95% localization success rate and takes less than 2.0-m average traveling distance to localize the mobile robot.